{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-agent-cooperation-and-the-emergence-of","title":"Multi-Agent Cooperation and the Emergence of (Natural) Language","arxiv_id":"1612.07182","date":"2016-12-21","proceeding":null,"authors":["Angeliki Lazaridou","Alexander Peysakhovich","Marco Baroni"],"abstract":"The current mainstream approach to train natural language systems is to\nexpose them to large amounts of text. This passive learning is problematic if\nwe are interested in developing interactive machines, such as conversational\nagents. We propose a framework for language learning that relies on multi-agent\ncommunication. We study this learning in the context of referential games. In\nthese games, a sender and a receiver see a pair of images. The sender is told\none of them is the target and is allowed to send a message from a fixed,\narbitrary vocabulary to the receiver. The receiver must rely on this message to\nidentify the target. Thus, the agents develop their own language interactively\nout of the need to communicate. We show that two networks with simple\nconfigurations are able to learn to coordinate in the referential game. We\nfurther explore how to make changes to the game environment to cause the \"word\nmeanings\" induced in the game to better reflect intuitive semantic properties\nof the images. In addition, we present a simple strategy for grounding the\nagents' code into natural language. Both of these are necessary steps towards\ndeveloping machines that are able to communicate with humans productively.","url_abs":"http://arxiv.org/abs/1612.07182v2","url_pdf":"http://arxiv.org/pdf/1612.07182v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-agent-cooperation-and-the-emergence-of","repo_url":"https://github.com/pranavmodi/language-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.07182","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.07182"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/pranavmodi/language-learning","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"listed":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"73c5542f1139fae8","entry":"shuffle_image_activations","repo":"pranavmodi/language-learning","repo_kind":"listed","path":"game.py","file_url":"https://github.com/pranavmodi/language-learning/blob/HEAD/game.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"73c5542f1139fae8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}